The book's first five chapters form the basis of a traditional, introduction to probability and random variables. In addition to the standard topics, it offers optional sections on modeling, computer methods, combinatories, reliability, and entropy. Chapters 4 through 9 can accommodate a one-semester senior/first-year graduate course on random processes and linear systems, as well as Markov chains and queuing theory. Additional coverage includes cyclostationary random processes, Fourier series and Karhunen-Loeve expansion, continuity, derivatives and integrals, amplitude modulation. Wiener and Kalman filters, and time reversed Markov chains. Features Chapter overviews: brief introduction outlining chapter coverage and learning objectives. Chapter summaries: concise, easy-reference sections providing quick overviews of each chapter's major topics. Checklist of important terms. Annotated references: suggestions of timely resources for additional coverage of critical material. Numerous examples: a wide selection of fully worked-out real-world examples. Problems: over 700 in all.

Chapter 1 Probability Models in Electrical
Chapter 2 Basic Concepts of Probability Theory
Chapter 3 Discrete Random Variables
Chapter 4 One Random Variable
Chapter 5 Pairs of Random Variables
Chapter 6 Vector Random Variables
Chapter 7 Sums of Random Variables and Long-Term Averages
Chapter 8 Statistics
Chapter 9 Random Processes
Chapter 10 Analysis and Processing of Random Signals
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